Overview

Dataset statistics

Number of variables15
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.1 KiB
Average record size in memory235.1 B

Variable types

Categorical2
Numeric13

Alerts

NEUMONIA is highly correlated with EDAD and 11 other fieldsHigh correlation
EDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
DIABETES is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
EPOC is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
ASMA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
INMUSUPR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
HIPERTENSION is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OTRA_COM is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CARDIOVASCULAR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OBESIDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
RENAL_CRONICA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
TABAQUISMO is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CLASIFICACION_FINAL is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
NEUMONIA is highly correlated with EDAD and 11 other fieldsHigh correlation
EDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
DIABETES is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
EPOC is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
ASMA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
INMUSUPR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
HIPERTENSION is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OTRA_COM is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CARDIOVASCULAR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OBESIDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
RENAL_CRONICA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
TABAQUISMO is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CLASIFICACION_FINAL is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
NEUMONIA is highly correlated with EDAD and 11 other fieldsHigh correlation
EDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
DIABETES is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
EPOC is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
ASMA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
INMUSUPR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
HIPERTENSION is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OTRA_COM is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CARDIOVASCULAR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OBESIDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
RENAL_CRONICA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
TABAQUISMO is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CLASIFICACION_FINAL is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
FECHA_DEF is highly correlated with DIABETES and 4 other fieldsHigh correlation
NEUMONIA is highly correlated with EDAD and 11 other fieldsHigh correlation
EDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
DIABETES is highly correlated with FECHA_DEF and 12 other fieldsHigh correlation
EPOC is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
ASMA is highly correlated with FECHA_DEF and 12 other fieldsHigh correlation
INMUSUPR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
HIPERTENSION is highly correlated with FECHA_DEF and 12 other fieldsHigh correlation
OTRA_COM is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CARDIOVASCULAR is highly correlated with FECHA_DEF and 12 other fieldsHigh correlation
OBESIDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
RENAL_CRONICA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
TABAQUISMO is highly correlated with FECHA_DEF and 12 other fieldsHigh correlation
CLASIFICACION_FINAL is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
FECHA_DEF is uniformly distributed Uniform

Reproduction

Analysis started2021-12-10 07:33:19.947620
Analysis finished2021-12-10 07:33:37.290489
Duration17.34 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

FECHA_DEF
Categorical

HIGH CORRELATION
UNIFORM

Distinct49
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2020-08-29
 
2
2020-08-31
 
2
2020-08-21
 
2
2020-08-08
 
2
2020-08-13
 
2
Other values (44)
69 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)24.1%

Sample

1st row2020-07-21
2nd row2020-08-06
3rd row2020-08-08
4th row2020-08-08
5th row2020-08-10

Common Values

ValueCountFrequency (%)
2020-08-292
 
2.5%
2020-08-312
 
2.5%
2020-08-212
 
2.5%
2020-08-082
 
2.5%
2020-08-132
 
2.5%
2020-08-172
 
2.5%
2020-09-142
 
2.5%
2020-08-162
 
2.5%
2020-08-252
 
2.5%
2020-08-222
 
2.5%
Other values (39)59
74.7%

Length

2021-12-10T01:33:37.343894image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-08-292
 
2.5%
2020-09-092
 
2.5%
2020-09-102
 
2.5%
2020-08-152
 
2.5%
2020-08-182
 
2.5%
2020-09-032
 
2.5%
2020-08-302
 
2.5%
2020-08-312
 
2.5%
2020-09-022
 
2.5%
2020-08-282
 
2.5%
Other values (39)59
74.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SEXO
Categorical

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
Mujer
40 
Hombre
39 

Length

Max length6
Median length5
Mean length5.493670886
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMujer
2nd rowMujer
3rd rowHombre
4th rowMujer
5th rowHombre

Common Values

ValueCountFrequency (%)
Mujer40
50.6%
Hombre39
49.4%

Length

2021-12-10T01:33:37.444143image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-10T01:33:37.513172image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
mujer40
50.6%
hombre39
49.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NEUMONIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.50632911
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:37.575656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median6
Q316
95-th percentile34.1
Maximum42
Range41
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation11.06956259
Coefficient of variation (CV)1.053608969
Kurtosis0.5392938306
Mean10.50632911
Median Absolute Deviation (MAD)5
Skewness1.179865868
Sum830
Variance122.5352158
MonotonicityNot monotonic
2021-12-10T01:33:37.691545image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
120
25.3%
29
 
11.4%
46
 
7.6%
134
 
5.1%
34
 
5.1%
153
 
3.8%
223
 
3.8%
142
 
2.5%
232
 
2.5%
172
 
2.5%
Other values (18)24
30.4%
ValueCountFrequency (%)
120
25.3%
29
11.4%
34
 
5.1%
46
 
7.6%
62
 
2.5%
72
 
2.5%
82
 
2.5%
91
 
1.3%
112
 
2.5%
122
 
2.5%
ValueCountFrequency (%)
421
1.3%
411
1.3%
381
1.3%
351
1.3%
341
1.3%
311
1.3%
291
1.3%
282
2.5%
251
1.3%
232
2.5%

EDAD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.8101266
Minimum30
Maximum2240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:37.814043image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile46.9
Q177
median275
Q3821.5
95-th percentile1667.9
Maximum2240
Range2210
Interquartile range (IQR)744.5

Descriptive statistics

Standard deviation564.1244652
Coefficient of variation (CV)1.064767238
Kurtosis0.8931951529
Mean529.8101266
Median Absolute Deviation (MAD)220
Skewness1.277319255
Sum41855
Variance318236.4122
MonotonicityNot monotonic
2021-12-10T01:33:37.929901image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1282
 
2.5%
472
 
2.5%
822
 
2.5%
782
 
2.5%
632
 
2.5%
662
 
2.5%
8071
 
1.3%
711
 
1.3%
691
 
1.3%
721
 
1.3%
Other values (63)63
79.7%
ValueCountFrequency (%)
301
1.3%
311
1.3%
391
1.3%
461
1.3%
472
2.5%
491
1.3%
551
1.3%
571
1.3%
581
1.3%
632
2.5%
ValueCountFrequency (%)
22401
1.3%
20281
1.3%
19831
1.3%
19281
1.3%
16391
1.3%
16001
1.3%
15801
1.3%
14251
1.3%
13261
1.3%
12231
1.3%

DIABETES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.89873418
Minimum1
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:38.161622image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q320
95-th percentile47.3
Maximum219
Range218
Interquartile range (IQR)18

Descriptive statistics

Standard deviation30.13712037
Coefficient of variation (CV)1.783395138
Kurtosis29.10113528
Mean16.89873418
Median Absolute Deviation (MAD)7
Skewness4.927174802
Sum1335
Variance908.246024
MonotonicityNot monotonic
2021-12-10T01:33:38.277542image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
213
16.5%
112
15.2%
45
 
6.3%
84
 
5.1%
153
 
3.8%
243
 
3.8%
63
 
3.8%
183
 
3.8%
262
 
2.5%
362
 
2.5%
Other values (23)29
36.7%
ValueCountFrequency (%)
112
15.2%
213
16.5%
32
 
2.5%
45
 
6.3%
51
 
1.3%
63
 
3.8%
71
 
1.3%
84
 
5.1%
91
 
1.3%
102
 
2.5%
ValueCountFrequency (%)
2191
1.3%
1421
1.3%
531
1.3%
501
1.3%
471
1.3%
401
1.3%
391
1.3%
362
2.5%
331
1.3%
301
1.3%

EPOC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.51898734
Minimum1
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:38.393432image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median8
Q324
95-th percentile58.3
Maximum225
Range224
Interquartile range (IQR)22

Descriptive statistics

Standard deviation28.99861215
Coefficient of variation (CV)1.565885413
Kurtosis32.87798973
Mean18.51898734
Median Absolute Deviation (MAD)6
Skewness4.919963919
Sum1463
Variance840.9195067
MonotonicityNot monotonic
2021-12-10T01:33:38.500265image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
221
26.6%
47
 
8.9%
84
 
5.1%
13
 
3.8%
163
 
3.8%
63
 
3.8%
123
 
3.8%
203
 
3.8%
492
 
2.5%
322
 
2.5%
Other values (26)28
35.4%
ValueCountFrequency (%)
13
 
3.8%
221
26.6%
31
 
1.3%
47
 
8.9%
51
 
1.3%
63
 
3.8%
84
 
5.1%
101
 
1.3%
111
 
1.3%
123
 
3.8%
ValueCountFrequency (%)
2251
1.3%
661
1.3%
641
1.3%
611
1.3%
581
1.3%
492
2.5%
461
1.3%
431
1.3%
421
1.3%
381
1.3%

ASMA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.73417722
Minimum2
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:38.616159image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median8
Q324
95-th percentile60.2
Maximum225
Range223
Interquartile range (IQR)22

Descriptive statistics

Standard deviation29.1260342
Coefficient of variation (CV)1.554700474
Kurtosis32.13529587
Mean18.73417722
Median Absolute Deviation (MAD)6
Skewness4.853425224
Sum1480
Variance848.3258682
MonotonicityNot monotonic
2021-12-10T01:33:38.732079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
224
30.4%
47
 
8.9%
64
 
5.1%
84
 
5.1%
163
 
3.8%
182
 
2.5%
322
 
2.5%
442
 
2.5%
222
 
2.5%
212
 
2.5%
Other values (23)27
34.2%
ValueCountFrequency (%)
224
30.4%
31
 
1.3%
47
 
8.9%
64
 
5.1%
84
 
5.1%
101
 
1.3%
112
 
2.5%
122
 
2.5%
131
 
1.3%
163
 
3.8%
ValueCountFrequency (%)
2251
1.3%
671
1.3%
651
1.3%
621
1.3%
601
1.3%
501
1.3%
481
1.3%
451
1.3%
442
2.5%
381
1.3%

INMUSUPR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.13924051
Minimum2
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:38.847939image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median8
Q325
95-th percentile61.5
Maximum225
Range223
Interquartile range (IQR)23

Descriptive statistics

Standard deviation31.64698478
Coefficient of variation (CV)1.571409049
Kurtosis23.35671172
Mean20.13924051
Median Absolute Deviation (MAD)6
Skewness4.194198619
Sum1591
Variance1001.531646
MonotonicityNot monotonic
2021-12-10T01:33:38.963824image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
224
30.4%
48
 
10.1%
124
 
5.1%
163
 
3.8%
203
 
3.8%
63
 
3.8%
83
 
3.8%
223
 
3.8%
442
 
2.5%
282
 
2.5%
Other values (21)24
30.4%
ValueCountFrequency (%)
224
30.4%
48
 
10.1%
51
 
1.3%
63
 
3.8%
71
 
1.3%
83
 
3.8%
101
 
1.3%
124
 
5.1%
141
 
1.3%
163
 
3.8%
ValueCountFrequency (%)
2251
1.3%
1281
1.3%
681
1.3%
661
1.3%
611
1.3%
601
1.3%
502
2.5%
461
1.3%
442
2.5%
381
1.3%

HIPERTENSION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.65822785
Minimum1
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:39.079712image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q319.5
95-th percentile45.1
Maximum220
Range219
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation26.61929418
Coefficient of variation (CV)1.815996753
Kurtosis46.07508662
Mean14.65822785
Median Absolute Deviation (MAD)5
Skewness6.10499269
Sum1158
Variance708.5868225
MonotonicityNot monotonic
2021-12-10T01:33:39.179954image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
115
19.0%
210
12.7%
38
 
10.1%
65
 
6.3%
175
 
6.3%
124
 
5.1%
233
 
3.8%
462
 
2.5%
92
 
2.5%
242
 
2.5%
Other values (20)23
29.1%
ValueCountFrequency (%)
115
19.0%
210
12.7%
38
10.1%
41
 
1.3%
52
 
2.5%
65
 
6.3%
71
 
1.3%
92
 
2.5%
101
 
1.3%
124
 
5.1%
ValueCountFrequency (%)
2201
1.3%
501
1.3%
462
2.5%
451
1.3%
371
1.3%
332
2.5%
311
1.3%
292
2.5%
271
1.3%
261
1.3%

OTRA_COM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.63291139
Minimum1
Maximum251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:39.302411image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median8
Q329
95-th percentile156.4
Maximum251
Range250
Interquartile range (IQR)27

Descriptive statistics

Standard deviation53.44927555
Coefficient of variation (CV)1.744831722
Kurtosis6.446475831
Mean30.63291139
Median Absolute Deviation (MAD)6
Skewness2.617370619
Sum2420
Variance2856.825057
MonotonicityNot monotonic
2021-12-10T01:33:39.418369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
222
27.8%
48
 
10.1%
64
 
5.1%
124
 
5.1%
323
 
3.8%
73
 
3.8%
203
 
3.8%
162
 
2.5%
462
 
2.5%
12
 
2.5%
Other values (24)26
32.9%
ValueCountFrequency (%)
12
 
2.5%
222
27.8%
48
 
10.1%
64
 
5.1%
73
 
3.8%
81
 
1.3%
101
 
1.3%
124
 
5.1%
152
 
2.5%
162
 
2.5%
ValueCountFrequency (%)
2511
1.3%
2261
1.3%
2041
1.3%
1601
1.3%
1561
1.3%
1401
1.3%
1281
1.3%
1241
1.3%
1181
1.3%
651
1.3%

CARDIOVASCULAR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.43037975
Minimum1
Maximum226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:39.534292image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median8
Q323.5
95-th percentile58
Maximum226
Range225
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation29.02063083
Coefficient of variation (CV)1.574608404
Kurtosis33.51392483
Mean18.43037975
Median Absolute Deviation (MAD)6
Skewness4.979871023
Sum1456
Variance842.197014
MonotonicityNot monotonic
2021-12-10T01:33:39.650191image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
221
26.6%
48
 
10.1%
214
 
5.1%
13
 
3.8%
203
 
3.8%
83
 
3.8%
283
 
3.8%
123
 
3.8%
52
 
2.5%
62
 
2.5%
Other values (24)27
34.2%
ValueCountFrequency (%)
13
 
3.8%
221
26.6%
48
 
10.1%
52
 
2.5%
62
 
2.5%
71
 
1.3%
83
 
3.8%
101
 
1.3%
111
 
1.3%
123
 
3.8%
ValueCountFrequency (%)
2261
1.3%
661
1.3%
651
1.3%
582
2.5%
492
2.5%
451
1.3%
431
1.3%
421
1.3%
361
1.3%
351
1.3%

OBESIDAD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.08860759
Minimum1
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:39.750491image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q323
95-th percentile57.2
Maximum222
Range221
Interquartile range (IQR)21

Descriptive statistics

Standard deviation33.09705614
Coefficient of variation (CV)1.733864347
Kurtosis20.23353931
Mean19.08860759
Median Absolute Deviation (MAD)6
Skewness4.074617517
Sum1508
Variance1095.415125
MonotonicityNot monotonic
2021-12-10T01:33:39.866381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
216
20.3%
19
 
11.4%
35
 
6.3%
144
 
5.1%
183
 
3.8%
63
 
3.8%
73
 
3.8%
362
 
2.5%
282
 
2.5%
262
 
2.5%
Other values (23)30
38.0%
ValueCountFrequency (%)
19
11.4%
216
20.3%
35
 
6.3%
42
 
2.5%
52
 
2.5%
63
 
3.8%
73
 
3.8%
81
 
1.3%
91
 
1.3%
102
 
2.5%
ValueCountFrequency (%)
2221
1.3%
1361
1.3%
1261
1.3%
591
1.3%
571
1.3%
531
1.3%
521
1.3%
451
1.3%
411
1.3%
371
1.3%

RENAL_CRONICA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.44303797
Minimum1
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:39.982333image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median8
Q324
95-th percentile60.4
Maximum225
Range224
Interquartile range (IQR)22

Descriptive statistics

Standard deviation31.64455915
Coefficient of variation (CV)1.627552196
Kurtosis24.41420215
Mean19.44303797
Median Absolute Deviation (MAD)6
Skewness4.363639889
Sum1536
Variance1001.378124
MonotonicityNot monotonic
2021-12-10T01:33:40.198529image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
221
26.6%
47
 
8.9%
164
 
5.1%
13
 
3.8%
83
 
3.8%
123
 
3.8%
223
 
3.8%
63
 
3.8%
272
 
2.5%
302
 
2.5%
Other values (26)28
35.4%
ValueCountFrequency (%)
13
 
3.8%
221
26.6%
31
 
1.3%
47
 
8.9%
51
 
1.3%
63
 
3.8%
71
 
1.3%
83
 
3.8%
101
 
1.3%
111
 
1.3%
ValueCountFrequency (%)
2251
1.3%
1371
1.3%
642
2.5%
601
1.3%
571
1.3%
491
1.3%
481
1.3%
421
1.3%
411
1.3%
351
1.3%

TABAQUISMO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.48101266
Minimum1
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:40.305366image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median8
Q324
95-th percentile58.2
Maximum223
Range222
Interquartile range (IQR)22

Descriptive statistics

Standard deviation31.34704859
Coefficient of variation (CV)1.609107757
Kurtosis24.2781373
Mean19.48101266
Median Absolute Deviation (MAD)6
Skewness4.338340509
Sum1539
Variance982.6374554
MonotonicityNot monotonic
2021-12-10T01:33:40.421269image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
223
29.1%
47
 
8.9%
84
 
5.1%
203
 
3.8%
282
 
2.5%
342
 
2.5%
52
 
2.5%
62
 
2.5%
242
 
2.5%
112
 
2.5%
Other values (28)30
38.0%
ValueCountFrequency (%)
11
 
1.3%
223
29.1%
31
 
1.3%
47
 
8.9%
52
 
2.5%
62
 
2.5%
84
 
5.1%
101
 
1.3%
112
 
2.5%
122
 
2.5%
ValueCountFrequency (%)
2231
1.3%
1351
1.3%
671
1.3%
601
1.3%
581
1.3%
571
1.3%
491
1.3%
451
1.3%
421
1.3%
401
1.3%

CLASIFICACION_FINAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.36708861
Minimum2
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size760.0 B
2021-12-10T01:33:40.537194image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median12
Q337.5
95-th percentile76.4
Maximum97
Range95
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation25.61008276
Coefficient of variation (CV)1.051011188
Kurtosis0.7899258645
Mean24.36708861
Median Absolute Deviation (MAD)9
Skewness1.250589739
Sum1925
Variance655.8763389
MonotonicityNot monotonic
2021-12-10T01:33:40.653079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
322
27.8%
67
 
8.9%
243
 
3.8%
483
 
3.8%
93
 
3.8%
123
 
3.8%
333
 
3.8%
183
 
3.8%
303
 
3.8%
22
 
2.5%
Other values (24)27
34.2%
ValueCountFrequency (%)
22
 
2.5%
322
27.8%
51
 
1.3%
67
 
8.9%
81
 
1.3%
93
 
3.8%
101
 
1.3%
123
 
3.8%
151
 
1.3%
161
 
1.3%
ValueCountFrequency (%)
972
2.5%
892
2.5%
751
1.3%
741
1.3%
671
1.3%
651
1.3%
611
1.3%
561
1.3%
531
1.3%
511
1.3%

Interactions

2021-12-10T01:33:35.654694image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:21.947017image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.152552image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.262048image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.411905image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.461631image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.602409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.667588image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.839402image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.995556image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.214388image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.317253image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.582967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.839580image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.038050image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.237197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.340192image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.496594image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.530657image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.687087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.736583image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.939644image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.180474image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.299038image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.517791image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.667587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.924226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.187734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.321793image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.525120image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.581178image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.715606image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.771699image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.921473image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.024264image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.258618image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.383651image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.602502image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.736644image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.024491image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.272415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.406500image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.609730image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.665857image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.800252image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.849809image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.006153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.124532image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.343265image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.461761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.687111image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.821294image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.109159image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.350490image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.506747image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.694407image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.728378image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.884901image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.934457image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.075146image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.193525image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.443501image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.530805image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.780873image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.905910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.193810image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.419534image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.606987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.779056image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.813053image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.962982image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.003452image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.153292image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.293763image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.528133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.615483image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.865520image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.990587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.271959image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.504215image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.691607image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.857161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.897701image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.047600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.088169image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.237905image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.394001image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.612737image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.700130image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.965758image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.084347image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.356602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.604455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.776262image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.941809image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.966722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.132244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.172745image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.306964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.472142image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.697457image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.784711image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.050375image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.168996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.441290image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.689104image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.860907image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.010867image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.060486image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.201270image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.250887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.391579image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.556792image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.782096image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.862823image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.150615image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.253677image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.541562image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.789320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.939051image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.095486image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.145136image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.286008image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.335564image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.476191image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.641377image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.860240image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.963088image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.250853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.322702image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.610633image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.873940image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.023697image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.180163image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.214194image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.370625image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.404560image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.576436image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.710402image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:31.944914image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.047710image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.335565image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.407317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.695248image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:22.974204image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.108379image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.258236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.298843image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.448768image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.489272image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.670170image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.810642image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.029532image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.147949image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.404622image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.491932image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:36.773359image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:23.058819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:24.177372image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:25.327296image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:26.367865image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:27.533383image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:28.573853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:29.754818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:30.910879image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:32.114148image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:33.232589image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:34.489234image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T01:33:35.570075image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2021-12-10T01:33:40.753321image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-10T01:33:40.953928image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-10T01:33:41.138953image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-10T01:33:41.308202image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-10T01:33:41.424161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-10T01:33:36.942718image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-10T01:33:37.190185image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FECHA_DEFSEXONEUMONIAEDADDIABETESEPOCASMAINMUSUPRHIPERTENSIONOTRA_COMCARDIOVASCULAROBESIDADRENAL_CRONICATABAQUISMOCLASIFICACION_FINAL
02020-07-21Mujer16612221221222
12020-08-06Mujer13912221221223
22020-08-08Hombre19421221212223
32020-08-08Mujer17112221222223
42020-08-10Hombre16622222222222
52020-08-11Hombre15712221212223
62020-08-12Hombre16312222221223
72020-08-13Hombre4275788857878812
82020-08-13Mujer26312221221223
92020-08-14Hombre321356663656568

Last rows

FECHA_DEFSEXONEUMONIAEDADDIABETESEPOCASMAINMUSUPRHIPERTENSIONOTRA_COMCARDIOVASCULAROBESIDADRENAL_CRONICATABAQUISMOCLASIFICACION_FINAL
692020-09-15Hombre316366665666659
702020-09-16Hombre23122221222123
712020-09-20Mujer213024443443445
722020-09-26Hombre212844442444446
732020-10-06Hombre16922222222223
742020-10-08Mujer17811221221223
752020-10-11Mujer14621222221213
762020-10-28Mujer14712222221223
772020-12-15Mujer13022222222123
782020-12-17Hombre15812221212123